DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs
This addresses IP protection for organizations using DNNs in cloud services, but it is incremental as it builds on existing security concerns and defense strategies.
The authors tackled the problem of intellectual property theft in deep neural networks by developing DeepPeep, a two-stage attack method that reverse-engineers the architecture of compact DNNs, and proposed Secure MobileNet-V1 as a defense, which reduced inference latency by ≈60% and improved predictive performance by ≈2% with low overhead.
The remarkable predictive performance of deep neural networks (DNNs) has led to their adoption in service domains of unprecedented scale and scope. However, the widespread adoption and growing commercialization of DNNs have underscored the importance of intellectual property (IP) protection. Devising techniques to ensure IP protection has become necessary due to the increasing trend of outsourcing the DNN computations on the untrusted accelerators in cloud-based services. The design methodologies and hyper-parameters of DNNs are crucial information, and leaking them may cause massive economic loss to the organization. Furthermore, the knowledge of DNN's architecture can increase the success probability of an adversarial attack where an adversary perturbs the inputs and alter the prediction. In this work, we devise a two-stage attack methodology "DeepPeep" which exploits the distinctive characteristics of design methodologies to reverse-engineer the architecture of building blocks in compact DNNs. We show the efficacy of "DeepPeep" on P100 and P4000 GPUs. Additionally, we propose intelligent design maneuvering strategies for thwarting IP theft through the DeepPeep attack and proposed "Secure MobileNet-V1". Interestingly, compared to vanilla MobileNet-V1, secure MobileNet-V1 provides a significant reduction in inference latency ($\approx$60%) and improvement in predictive performance ($\approx$2%) with very-low memory and computation overheads.